The overall architecture of our network is shown in Fig. REF , which consists of both standard RNN and closed-form IMU integration modules.
Specifically, the RNN module is used to represent the IMU model (de-noise, intrinsic, and others),
which takes raw measurements as input and calculates refined values.
The differentiable integration module is used to generate \(\Delta {\mathbf {\gamma }}, \Delta {\mathbf {\beta }}\)  and \(\Delta {\mathbf {q}}\)  for loss functions.
We note that, an alternative solution is to use RNN layers to directly regress \(\Delta {\mathbf {\gamma }}, \Delta {\mathbf {\beta }}\)  and \(\Delta {\mathbf {q}}\)  instead of adding the extra integration blocks. However, integration computation requires a combination of operations in
Euclidean and \(SO(3)\)  space (especially the ones relate to rotation terms), which can not be well expressed by standard light-weighted RNN layers.
In our method,
we use stacked bidirectional LSTM connected by fully-connected (FC) layers for RNN, and utilize the parameter-free closed-form differential equations [1]} for the integration module.
